Cleaning
AI-Integrated Autonomous Robotics for Solar Panel Cleaning and Predictive Maintenance
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Overview
この研究では、リアルタイム監視、予測分析、インテリジェントな清掃を組み合わせたAI統合型自律ロボットシステムを提案し、太陽パネルの性能を向上させます。システムはCNN-LSTMベースの故障検出、強化学習（DQN）駆動のロボット清掃、エッジAI分析を統合し、低遅延での意思決定を実現します。サーマルおよびLiDAR装備のドローンがパネルの故障を検出し、地上ロボットがリアルタイムのほこりと温度データに基づいてパネル表面を清掃します。このシステムは平均清掃効率91.3％を達成し、ほこり密度を3.9から0.28 mg/m³に減少させ、汚れがひどいパネルで最大31.2％のエネルギー出力を回復させます。
Detailed specifications
Motion & kinematics1
- Fault Detection Accuracy
- 92.3%
Power & battery1
- Energy Restoration
- up to 31.2%
Other12
- Applications
- industrial_cleaning,disinfection
- Sub Category
- solar_panel_cleaning
- Dust Reduction
- 3.9 to 0.28 mg/m³
- Cleaning Method
- scrubbing
- Edge Ai Latency
- 47.2 ms
- Navigation Type
- lidar_slam,vision,hybrid
- Deployment Notes
- Deployed at Sitapura, Jaipur, India for a 72-hour field test.
- Industries Served
- solar_farms
- Obstacle Avoidance
- true
- Availability Status
- research-only
- Cleaning Efficiency
- 91.3%
- Additional Information
- - Achieves 91.3% cleaning efficiency with RL-based optimization. - Reduces dust density from 3.9 to 0.28 mg/m³. - Restores up to 31.2% energy output on heavily soiled panels. - Uses CNN-LSTM for fault detection with 92.3% accuracy. - Edge AI reduces latency by 63% compared to cloud processing. - Deployed in Sitapura, Jaipur for field testing.
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